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Computer scientists from the University of California, San Diego are developing an algorithm that can determine what subculture you belong to by analysing photos of you. Among the various "urban tribes" the algorithm can identify you as being a member of are the categories of biker, surfer, punk, hipster or goth. "In the past few years there has been a massive inﬂux of images provided by social media; Facebook alone receives around 300 million photos a day. The abundance of social media presents a compelling opportunity to analyse the social identity of individuals captured within images," states the study, which was presented at the British Machine Vision Conference in September.

The researchers believe the tool could be used in a variety of applications, including marketing strategies and psychological sociology. Currently the algorithm can correctly identify subcultures 48 percent of the time, but the developers are trying to improve this to make it at least as accurate as a human would be.

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The algorithm is able to analyse a photo of a human by dividing their characteristics into six sections: face, head, top of the head, neck, torso and arms. From there it can examine various attributes including haircuts, hair colours, makeup, jewellery and tattoos, as well as being able to distinguish the various colours and textures in each section.

In order to train the algorithm to recognise the various subcultures, the researchers fed through labelled photos of the groups they wanted it to be able to recognise until it was able to identify people in photos without an attached label. The various labels were also heavily associated with a range of indoor and outdoor venues, such as bars, clubs and concerts.

One problem highlighted in the study was the fragmentation and variances within the social categories, as well as individuals who exhibited features of multiple urban tribes. They tackled this by focusing on "calculating meaningful group features and models" rather than attempting to classify characteristics that could just identify individuals.

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Altogether the dataset consisted of 11 classes of subculture, with around 100 different labelled images per class. "The exhaustive and promising experimental results show that it is possible to extract semantic meaning from social media group photos, opening opportunities for the previously mentioned applications," said the study.

During their research the group assembled a huge library of pictures of various groups of people, which they intend to offer to other research groups. The next step for them is to test their datasets on humans in order to work out exactly how accurate the algorithms perform in comparison.